Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-local-hardware-sizing-creators

1. What is the core idea behind "Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities"?

   1. Whether a model runs well — or at all — depends on the hardware you put under it. Here is the practical map of what hardware can run which class of model.
   2. total cost of ownership
   3. failure note
   4. recall

2. Which term best describes a foundational idea in "Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities"?

   1. unified memory
   2. VRAM
   3. KV cache
   4. quantization

3. A learner studying Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities would need to understand which concept?

   1. VRAM
   2. KV cache
   3. unified memory
   4. quantization

4. Which of these is directly relevant to Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

   1. VRAM
   2. unified memory
   3. quantization
   4. KV cache

5. Which of the following is a key point about Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

   1. A modern desktop CPU can run a 7-8B model at a few tokens per second
   2. Useful for batch processing where latency does not matter
   3. Dramatically slower than even a modest GPU
   4. Good for running a coding assistant in the background — bad for chat UX

6. Which of these does NOT belong in a discussion of Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

   1. total cost of ownership
   2. Useful for batch processing where latency does not matter
   3. A modern desktop CPU can run a 7-8B model at a few tokens per second
   4. Dramatically slower than even a modest GPU

7. Which statement is accurate regarding Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

   1. Pick the quantization you can tolerate quality-wise (Q4 is the sweet spot for most)
   2. Add 25% for KV cache, runtime, and the rest of your OS
   3. Decide which model class you actually need (7B, 13B, 30B, 70B)
   4. Buy hardware whose usable memory comfortably exceeds that number — not just barely matches it

8. Which of these does NOT belong in a discussion of Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

   1. total cost of ownership
   2. Pick the quantization you can tolerate quality-wise (Q4 is the sweet spot for most)
   3. Add 25% for KV cache, runtime, and the rest of your OS
   4. Decide which model class you actually need (7B, 13B, 30B, 70B)

9. What is the key insight about "The math: parameters times bytes" in the context of Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

   1. A 7-billion-parameter model at FP16 uses ~14GB. At Q4, ~4GB. At Q8, ~7GB.
   2. total cost of ownership
   3. failure note
   4. recall

10. What is the key insight about "Context length costs memory too" in the context of Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

    1. total cost of ownership
    2. The KV cache scales with context length. A 7B model at 32k context uses noticeably more memory than the same model at 4k.
    3. failure note
    4. recall

11. What is the key insight about "From the community" in the context of Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

    1. total cost of ownership
    2. failure note
    3. Apple Silicon hardware threads on r/LocalLLaMA are full of users discovering the macOS quirk that caps GPU usage at roug…
    4. recall

12. Which statement accurately describes an aspect of Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

    1. total cost of ownership
    2. failure note
    3. recall
    4. An LLM has to fit into memory before it can run. On a discrete GPU, that means VRAM.

13. What does working with Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities typically involve?

    1. Apple's unified memory architecture means a 64GB Mac Studio can hold a 70B-class model that a 24GB consumer GPU simply cannot.
    2. total cost of ownership
    3. failure note
    4. recall

14. Which of the following is true about Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities?

    1. total cost of ownership
    2. The big idea: pick the model first, then size the memory, then pick the hardware.
    3. failure note
    4. recall

15. Which best describes the scope of "Hardware Sizing for Local Models: VRAM, Unified Memory, and CPU-Only Realities"?

    1. It is unrelated to model-families workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on Whether a model runs well — or at all — depends on the hardware you put under it. Here is the practi
    4. It was deprecated in 2024 and no longer relevant